README27 February 2023Archive for data and code for:Repeated evolution of unorthodox feeding styles drives a negative correlation between foot size and bill length in hummingbirds. 2022. American Naturalist 000:000-000.Authors and contacts:Robert K. Colwell*, colwell@uconn.eduThiago F. Rangel*, thiago.rangel@ufg.brKarolina Fu?�kov�, karolina.fucikova@gmail.comDiego Sustaita, dsustaita@csusm.eduGregor M. Yanega, gregor.yanega@gmail.comAlejandro Rico-Guevara*, colibri@uw.edu*Corresponding authorsORCIDs: Colwell, https://orcid.org/0000-0002-1384-0354; Rangel, http://orcid.org/ 0000-0002-2001-7382; Fuc�kov�, http://orcid.org/0000-0002-2177-4692; Sustaita, https://orcid.org/0000-0001-9932-909X; Yanega, https://orcid.org/0000-0001-7896-7738; Rico-Guevara https://orcid.org/0000-0003-4067-5312Brief summary: We gathered records of hummingbirds clinging by their feet to feed legitimately as pollinators or illegitimately as nectar-robbers. We measured key features of bills and feet for 220 species of hummingbirds and compared the 66 known �clinger� species to the 144 presumed �non-clinger� species. Once the effects of phylogenetic signal, body size, and elevation above sea level are accounted for statistically, hummingbirds display a surprising, but functionally interpretable negative correlation. Clingers with short bills and long hallux (hind-toe) claws have evolved�independently�more than 20 times, and in every major clade.GUIDE TO THE CONTENTS OF THIS ZIP FILE AND RELATED ZENODO FILESNote: Set the viewing option in Harvard Dataverse to Tree view to visualize the folder hierarchy.Notes about file formats: Each text file (including this one) appears in two formats: .rtf (Rich Text Format) for easy reading, and .txt (Plain Text) for posterity, in the unlikely event that .rtf (unchanged since 2008) is someday obsolete. Each data file appears in two formats: .xlxs (Excel) to take advantage of color coding of hummingbird feeding styles and two-way scrolling (in Supplemental Spreadsheet S1: Morphometric and Behavioral Data), and .csv (Comma Separated Values) for posterity and interoperability. The Hummingbird Specimen Data Input file also appears in Tab-Separated Values (.txt) format, required for input to the 4D data processing procedures.WORKFLOW: This guide is organized around the workflow followed for this publication, summarized as follows, with reference to relevant files for data and code after the Steps.Step 1. Gather and record specimen data from museum round-skins and living hummingbirds.Step 2. Extract logs of arithmetic means of morphological measurements, among specimens, within species, for body weights and morphological measurements from the specimen data. Repeat for sexes separately. Rationale: The distribution of linear morphological traits among conspecific individuals is typically approximately symmetric (Normal), whereas the distributions of the same (mean) traits among species within a clade are typically right-skewed, often approximately log-normal (e.g., Smith, R. J., and W. L. Jungers. 1997. Body mass in comparative primatology. Journal of Human Evolution 32:523-559). Record results in Supplemental Spreadsheet S1: Morphometric and Behavioral Data.Step 3. Extract variance in logs of morphological measurements, among specimens, within species, to estimate measurement error variance for each morphological character. Rationale: See p. 282 and Eq. 31 in Warton, D. I., I. J. Wright, D. S. Falster, and M. Westoby. 2006. Bivariate line-fitting methods for allometry. Biological reviews 81:259-291.Step 4. Gather and record published descriptions or photographic evidence of feeding styles for hummingbird species, pooling styles to define Clingers vs. Non-Clingers. Record in Supplemental Spreadsheet S1: Morphometric and Behavioral Data. For details, see the section Behavioral Data: Feeding Styles in the publication.Step 5. Using data deposited in Dryad, by Jimmy Maguire (https://orcid.org/0000-0002-9562-5585), generate 3000 alternative phylogenies for 208 of the 220 species in our dataset.Step 6. Based on species means, extract PGLS residuals, filter out the effects of phylogeny (phylo-filtered data); filter out phylogeny, body size, and elevation a.s.l. (triple-filtered data), for each of 100 alternative phylogenies, randomly chosen for each analysis from the 3000 in Step 5. Repeat for sexes separately. Record means of scores, among phylogenies, in Supplemental Spreadsheet S1: Morphometric and Behavioral Data. Note: Because Supplemental Spreadsheet S1combines and presents all the key data for this study in a single table and is cited more than 20 times in the publication, it appears both as an online Supplement to the publication (in Excel .xlxs format, color coded, with two-dimensional scrolling under headings), and here in this Dataverse Repository, in .xlxs�as well as in .csv format as well, for posterity.Step 7. Produce graphical scattergrams for PGLS residuals, color-coded for feeding styles, for pairs of variables, visualizing phylogenetic uncertainly.Step 8. Compare 66 Clingers with 144 Non-Clingers for each morphological variable (univariate comparisons), using standard two-sample statistics (NHST and Effect Size). Step 9. Compute False Discovery Rate (FDR) to control for multiple comparisons.Step 10. Compute SMA (Standardized Major Axis) slopes and elevations (of fitted lines), with 95% CIs, for key pairs of filtered of morphological characters, for all using triple-filtered data. Repeat for Clingers, Non-clingers, and sexes separately. Compute False Discovery Rate (FDR) to control for multiple comparisons.Step 11. Compute SMA (Standardized Major Axis) slopes and elevations (of fitted lines), with 95% CIs, for allometry of key morphological characters, using phylo-filtered data. Repeat for Clingers, Non-clingers, and sexes separately. Compute False Discovery Rate (FDR) to control for multiple comparisons.GUIDE TO THE FOLDER STRUCTURE AND CONTENTS, with reference to the workflow Steps Note: Set the viewing option in Harvard Dataverse to Tree view to visualize this folder hierarchy.Files in the folder: Specimen data (Steps 1, 2, 3)Sub-Folder: 4D code procedures for processing Specimen DataNote_on_4th_Dimension_code (.rtf and .txt)Humm_Data_Means_Extractor (.rtf and .txt)Measurement_Error_Variance (.rtf and .txt)Sub-Folder: Input and output data files for processing Specimen DataColumn_headings_in_the_Specimen_Data_File (.rtf and .txt)Hummingbird_Specimen_Data_Input_12Dec2022 (.xlsx and .csv)Hummingbird_Specimen_Data_Means_Output._15Dec2022 (.xlsx and .csv)Measurement_Error_Variance_Output_15Dec2022 (.xlsx and .csv)Files in the folder: NEXUS data for phylogenies (Step 5)Modified_McGuire2014_alignment_README (.rtf and .txt)Modified_McGuire2014_alignment.nexFiles in the folder: PGLS code, input data, and output data (Steps 6 and 7)README_for_PGLS_code_input_and_output_data (.rtf and .txt). Note: The code and data described in the above readme file are deposited in ZENODO https://zenodo.org/record/7331866#.Y6tOgi-B3_Q 
Files in the folder: Summary Data (Supplemental Spreadsheet S1: Morphometric and Behavioral Data) (Steps 2, 4, and 6)Appendix_A_Morphometric_and_Behavioral_Data_14Dec2022 (.xlsx and .csv)Appendix_A_Metadata_27Dec2022 (.rtf and .txt)Files in the folder: Two-sample comparison of Clingers with Non-Clingers (Step 8)Two_sample_statistics_NHST_and_Effect_Size (.rtf and .txt)Sub-Folder: Data files for Two-sample ComparisonsTriple_Filtered_Hallux_Claw_Cling_NoClingTriple_Filtered_Culmen_and_Hallux_Claw (.xlsx and .txt)Files in the folder: R code procedure for False Discovery Rates using p.adjust (Step 9)FDR_Example (.rtf and .txt)Files in the folder: R code procedures and example data for analysis using smatr3 (Steps 10 and 11)R_code_for_RMA_Slope_and_Elevation_using_smatr3 (.rtf and .txt)Sub-Folder: Data files for R code for SMA ExamplesPhylo_filtered_Culmen (.xlsx and .csv)Phylo_filtered_Weight (.xlsx and .csv)Triple_Filtered_Culmen (.xlsx and .csv)Triple_Filtered_Hallux_Claw (.xlsx and .csv)Culmen_Allom_All_Groups_No_Outs (.xlsx and .csv)